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| Energy Savings Calculations:
“Nameplate Savings” Versus Data-Driven Operational Savings
Building owners and operators are presented a wide range of energy
saving technologies. The effort to evaluate them is daunting. Each has
different savings potentials, implementation costs and financial
results. The implementation costs and ROI are of course key
considerations in the selection process when considering Energy
Conservation Measures (ECMs).
One type of savings can be referred to as “equipment nameplate savings”. By this we are referring to replacement a piece of equipment with a new device that has lower energy consumption for producing the same amount of output. Changing out conventional lighting for LED lighting is a good example, as are high efficiency motors.
ECMs based on nameplate savings are easy to calculate and often end up being selected for that reason. Other technologies have energy savings impacts that cannot be calculated as easily. Analytics falls into this category. Analytics is a tool to identify issues, faults and opportunities for savings in the operation of our equipment systems. Analytics provide the information needed to make decisions about which actions should be taken to reduce energy waste, improve occupant comfort, reduce reactive maintenance cost and extend the useful life of large capital assets such as chillers. All of these benefits add back to the bottom line but not all are easily calculated or anticipated.
One of the challenges with analytics is that until you apply analytics you don’t know what you are going to find. A huge volume of case studies from a wide range of analytics companies show evidence of results, but there is no way to calculate those savings ahead of time. There is a wealth of anecdotal evidence, and experience with similar facilities can provide strong indications of benefits, but not a true ROI in advance of the project. And until you address the issues found you don’t save energy and money. So does this mean nameplate savings should be the ECM of choice versus savings that come from analytics?
Nameplate savings can appear simple and easily attainable but the actual savings that result may not be as simple as they appear. The following example demonstrates the issues:
Scenario: Let's say we are looking at replacing conventional fluorescent lighting with LED lighting on a floor of a building. First let's look at our assumptions:
Energy cost: $0.10 per kwh
Floor space: 3000 sq ft.
Existing lighting consumes 1.5 watts per square foot using T8 fluorescents.
LED lights will reduce that to .69 watts per square foot (46% reduction in consumption vs. T8 fluorescents).
Based on 56 hours per week of normal occupied lighting operation our savings would be:
So the LED lighting would save us $58 per month!
But what happens if the lights get overridden and run around the clock
for 15 days per month? Which is not an uncommon problem. That’s 240
wasted hours of operation! Let's take a look at the numbers:
While it's easy to say that our switch from T8’s to LEDs will save a certain amount of money that will only be true IF the operation of the lights stays within expected occupancy. If the lights run outside of occupied periods our savings will be less. In our example operation of lighting around the clock for 15 days a month will waste $50 per month -- as much money as the LEDs save! Improper operation of equipment systems like lighting and other major energy consuming loads is one of the operational issues that analytics regularly detects.
This example shows that data driven savings can easily meet or exceed “easy to calculate” nameplate savings options. It demonstrates that it’s not just about the rated consumption oof equipment. It's about how the equipment systems operate.
Using data to improve operational efficiency and lower operating costs is proven in thousands of sites delivered by a wide range of companies offering analytics software applications and services. Using data effectively gives us the ability to understand how our buildings are really operating – not how we hope they are operating.
About the Author
John Petze, C.E.M., is a partner in SkyFoundry, the developers of SkySpark™, an analytics platform for building, energy and equipment data. John has over 30 years of experience in building automation, energy management and M2M, having served in senior level positions for manufacturers of hardware and software products including Tridium, Andover Controls, and Cisco Systems. At SkyFoundry he is working to bring the next generation of information analytics to the “Internet of Things”.
More information on SkySpark® analytics is available at www.skyfoundry.com
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